To address the problems of low accuracy， difficult deployment and high calibration cost of visual manipulator in complex system environments， a robust joint modelling and optimization method for visual manipulators was proposed. Firstly， the subsystem models of the visual manipulator were integrated together， and the sample data such as servo motor rotation angles and manipulator end-effector coordinates were collected randomly in the workspace of the manipulator. Then， an Adaptive Multiple-Elites-guided Composite Differential Evolution algorithm with shift mechanism and Layered Optimization mechanism （AMECoDEs-LO） was proposed. Simultaneous optimization of the joint system parameters was completed by using the method of parameter identification. Principal Component Analysis （PCA） was performed by AMECoDEs-LO on stage data in the population， and with the idea of parameter dimensionality reduction， an implicit guidance for convergence accuracy and speed was realized. Experimental results show that under the cooperation of AMECoDEs-LO and the joint system model， the visual manipulator does not require additional instruments during calibration， achieving fast deployment and a 60% improvement in average accuracy compared to the conventional method. In the cases of broken manipulator linkages， reduced servo motor accuracy and increased camera positioning noise， the system still maintains high accuracy， which verifies the robustness of the proposed method.

Edge Computing （EC） and Simultaneous Wireless Information and Power Transfer （SWIPT） technologies can improve the performance of traditional networks， but they also increase the difficulty and complexity of system decision-making. The system decisions designed by optimization methods often have high computational complexity and are difficult to meet the real-time requirements of the system. Therefore， aiming at Wireless Sensor Network （WSN） assisted by EC and SWIPT， a mathematical model of system energy efficiency optimization was proposed by jointly considering beamforming， computing offloading and power control problems in the network. Then， concerning the non-convex and parameter coupling characteristics of this model， a joint optimization method based on deep reinforcement learning was proposed by designing information interchange process of the system. This method did not need to build an environmental model and adopted a reward function instead of the Critic network for action evaluation， which could reduce the difficulty of decision-making and improve the system real-time performance. Finally， based on the joint optimization method， an Improved Deep Deterministic Policy Gradient （IDDPG） algorithm was designed. Simulation comparisons were made with a variety of optimization algorithms and machine learning algorithms to verify the advantages of the joint optimization method in reducing the computational complexity and improving real-time performance of decision-making.

Aiming at the problem of malicious eavesdropping nodes in the energy limited multi-user Mobile Edge Computing （MEC） system， a joint Wireless Power Transfer （WPT） and MEC secure partial computing offloading programme was proposed. In order to minimize the energy consumption of the system Access Point （AP）， the AP energy transmission covariance matrix， local CPU frequency， user unloading bits， user offloading time allocation and user transmission power were jointly optimized under the constraints of computing delay， secure offloading and energy capture. For the AP energy consumption minimization was a non-convex problem， firstly， the original non-convex problem was transformed into a convex problem by Difference of Convex Algorithm （DCA）. Then， the optimal solution of the problem was obtained in semi-closed form by Lagrange duality method. When the number of computing tasks is 5 × 10⁵ bits， compared with local computing offloading and secure full computing offloading， the energy consumption of secure partial offloading scheme was reduced by 61.3% and 84.4%， respectively； when the distance between eavesdropping nodes exceeds 25 m， the energy consumed by the secure partial offloading scheme is much less than those of local computing offloading and secure full computing offloading. The simulation results show that the proposed scheme can effectively reduce AP power consumption and enhance system performance gain while ensuring the secure offloading of the physical layer.

Focusing on the needs of mobile application traffic monitoring and behavior analysis,a Hyper Text Transfer Protocol(HTTP)traffic structural signature extraction method was proposed to effectively identify the application to which mobile network traffic belongs. A self-developed Virtual Private Network(VPN)-based traffic collection tool was used to obtain the research data,which was able to accurately identify the application that each data stream belongs to. In the signature extraction stage,the signature composition was not pre-designed,and the structural signatures of the HTTP traffic were obtained through three steps of flow clustering,obtaining the longest common subsequence and character substitution. The signatures of 42 applications were extracted from 117 772 HTTP traffic to identify 50 387 HTTP traffic in test set. The proposed algorithm has the average accuracy of 99%,the average recall of 90. 63%,and the maximum false positive rate of single application of 0. 52%. The experimental results show that the proposed structural signature extraction method can effectively identify the traffic of mobile applications.

Considering the adoption of information and energy simultaneous transmission in wireless networks to improve the performance of wireless relay systems， a bidirectional transmission full-duplex relay system with self-energy recycling based on wireless radio frequency network was proposed by using Simultaneous Wireless Information and Power Transmission （SWIPT） technology. It is a new attempt to apply SWIPT in bidirectional full-duplex relay system. The energy-constrained destination node used the energy harvested from the relay and the loop channel to send feedback information， and the logical structure of the full-duplex relay system and the physical structure of the energy-constrained destination node were given. Then， the system performance was described by using the minimization of the system transmit power sum as the optimization target， the power allocation scheme was used for information decoding and energy harvest， the semi-definite programming， rank relaxation and Lagrange methods were used to transform the original non-convex optimization equation into a solvable convex optimization problem， and the solution of the problem was found. The relay transmission power， transmit beamforming vector and power allocation ratio were jointly optimized. Finally， the proposed system was compared with the traditional bidirectional transmission relay system by experimental simulator. The results verify that the self-energy recycling can not only eliminate self-interference， but also significantly optimize the system transmission power sum， and reveal that the proposed system has higher performance gain than the traditional bidirectional transmission system due to the combination of SWIPT technology and full-duplex relay system.

Scientists identify the species of whales based on the shape and the distinctive marks of the whale tails， but the process of recognition by human eyes and manual labeling is very cumbersome. The dataset of whale tail photo has the unbalanced data distribution， and some specific categories in the dataset have very few samples or even one sample. Besides， the samples have small individual differences and contain unknown categories， which leads to the difficulty in automatic labeling of whale identification by image classification. To solve the problem that metric learning is difficult to realize classification under this task， on the basis of Siamese Neural Network （SNN）， the training batches were constructed dynamically by using Linear Assignment Problem （LAP） algorithm in the training process of hard-negative sample mining. Firstly， image feature vectors were extracted from the training samples， and the similarity metric of feature vector was calculated. Then， LAP was used to assign sample pairs to the model， training sample batches were constructed dynamically according to the metric score matrix， and the difficult sample pairs were targeted by trained. Experimental results on a whale tail image dataset with unbalanced data distribution and CUB 200-2001 dataset show that， the proposed algorithm can achieve good results in learning minority classes and classifying fine-grained images.

Information Retrieval (IR) technology is widely used in automatic tracing from software documents to source codes, but Chinese document and source code are written in different languages, which leads to low accuracy of automatic tracing by using IR. In view of the above problems, an automatic tracing method of Chinese document to source code based on version control was proposed. Firstly, the similarity score between the documents and the source code was calculated by information retrieval method combined with text-to-source heuristic rules. Then the score was modified by the version update information which was submitted to the version control software during software development and maintenance. Finally, the tracing relationship between the Chinese document and source code was determined according to the set threshold. The experimental results show that the precision and recall of the proposed method have a certain improvement compared with the traditional IR method, and the tracing relationship missed in the traditional IR method can be extracted.

Focused on the limitation of monitoring distance, restriction of online transmission, large amount of information and that high-power data-link is disable to board small environment monitoring Unmanned Aerial Vehicle (UAV), a multiple-UAV environment monitoring task scheduling method considering 3G/4G network features was proposed. First, the time characteristic of 3G/4G network and the multiple-UAV environment monitoring task scheduling were combined, and this issue was modeled as Team Orienteering Problem with Time Window (TOPTW). Secondly, since the problem of huge computation and easily falling into local optimum, an Iterated Local Search (ILS) algorithm was proposed to get the optimization solution. Thirdly, a large amount of test data sets were applied into experiments to verify the feasibility and computing performance, and the comparative result between ILS and Ant Colony Algorithm (ACA) about the average profit and computing time were proposed. Last, the algorithm was applied in typical two UAV environment monitoring task scheduling under 3G/4G network. The results show that, the most profits received from ILS were worse than those from ACA. The average Gap of all test data sets was 1.09% and the largest was 10.8%. There were some results better than those in ACA. And the computing time of ILS was nearly reduced to a thousandth of the computing time of ACA. The experimental results show that ILS algorithm can fast solve the issue of multi-UAV environment monitoring task scheduling and effectively reduce the computing time with profit results in an acceptable scope.

In task decomposition and coordination for multi-robot, the problem of workload imbalance in task with partial order constraint still exists. To overcome this problem, a task coordination and workload balance method for multi-robot based on trading-tree was proposed. Firstly, the task decomposition problem satisfying partial order constraint was described as a constraint graph. Secondly, an initial task assignment strategy was proposed according to the directed weighted graph, and the problem of task coordination among multiple robots was solved by using the improved Dijkstra algorithm. At last, the strategy of workload balance was proposed to balance each robot's workload without violating any constraints via a protocol based on trading-tree. The experimental results show that, compared with Dijkstra algorithm, after finishing the strategy of workload balance, the efficiency signifiantly increases by 12% and the difference of workload reduces by 30%.

As the most widely used autonomous intra-domain routing protocol for large-scale network, the security of Open Shortest Path First (OSPF) is not only about the normal running of autonomous intra-domain, but also closely related to inter-domain even the whole network. Based on asymmetric encryption algorithm, the traditional digital signature solution can realize the security validation of end-to-end; however, it ignores the issue of point-to-point. Additionally, the problem of storage and extra overhead also needs to be solved urgently. On the basis of symmetrical encryption algorithm, a new solution named HS-OSPF was put forward. HS-OSPF extended the original two-level hierarchical structure as well as designed a reasonable, efficient key distribution and management scheme. The result shows that the shortcomings of traditional solution are overcome, key storage and system overhead are reduced and real-time of security communication is improved.

To deal with the huge spatial and temporal consumption caused by large-scale self-set data, the authors designed a self-set matching mechanism based on artificial immune Network Intrusion Detection System (NIDS). To improve the detection efficiency of the intrusion detection system, an efficient probability matching optimization mechanism was proposed. The authors first proved the relative concentration of the network data, and analyzed the validity of the probability matching mechanism by calculating the Average Search Length (ASL), then verified the fast matching efficiency of the mechanism through simulation experiments. The mechanism has been used in a project application in a new artificial immune network intrusion detection system based on self-set scale simplified mechanism, which has achieved satisfactory matching results.

A multi-person decision method based on the grey additive linguistic variables weighted aggregation operator is presented to solve the Network Security Risk Evaluation problems in mobile bank, in which the attribute values take the form of the grey additive linguistic variables(GALV). Firstly, some properties are defined, such as the concept and the relational calculation rules of grey additive linguistic variables. Then, some operators will be defined to solve the Network Security Risk Evaluation problems in mobile banking, such as grey additive linguistic weighted aggregation operator, and grey additive linguistic ordered weighted aggregation operator. At last, an example involved in mobile bank shows the effectiveness of this method.

Considering the correlation and dependence among indicators, a multi-person decision model based on analytic network process was designed. In this model, the network security evaluation index was elicited by means of analytic network process, and then, a bi-level programming based on weighted Euclidean distance was used to synthesize the individual decision-making results. An illustrative example was given to demonstrate the feasibility and validity of the proposed method. The result shows that the proposed model has higher credibility to evaluate the network security in commercial banks.

The universal approximation properties of hierarchical functional network were verified. Using this feature, the structure and learning algorithm of hierarchical functional network were used to obtain numerical approximation in the multiple numerical integral. Simulation results show that this method for numerical calculation of multiple numerical integrals is practical and effective, and has good computation precision.

As a multilevel optimization problem, foundation excavation optimization concerns many constraint conditions. With the analysis in hierarchy and relation of level design, a new collaborative optimization algorithm was proposed, which was distributed, hierarchical and of coevolution. Collaborative optimization was integrated with particle swarm optimization to speed up the evolution and simply the optimization process. Collision and disposal in optimization of subsystems, which belong to coevolution optimization system, were analyzed. At last, an experiment of parallel collaborative design with "anchored piles in row and waterproof curtain" was given to show its effectiveness.

Traditionally, text filtering based on support vector machine uses the vector space model to represent the text and user profile. Vector space model draws the noise into the system because it assumes that the word in the text is independent and it influences the performance of the filtering. The proposed method was based on vector support machine of semantic space in which text and user profile were represented by the semantic space. The proposed approach used the singular-value decomposition to derive a latent semantic space. User profile and filtering threshold could been got by training the support vector machine in the semantic space. And the similarity between the user profile and new text was computed by cosine measure, after the new text was mapped into the semantic space. Experimental results show that the filtering rate of our approach can get 98.67%.

The result that current web search engineer returned were ranked mainly by their hyperlink analyse, not their content. To take the search results as an order collection, we used item frenqency statistic and calculated item position in every page by certain formula, by which we calculated each pages relativity and re-ranked the collection. The experiment results show that the pages which meet the users needs were concentrated ahead. In this way, The precision was enhanced. It can help user find information rapidly.